Stochastic Approximation

This simple compact toolkit for designing and analyzing stochastic approximation algorithms requires only a basic understanding of probability and differential equations. Although powerful these algorithms have applications in control and communications engineering artificial intelligence and economic modeling. Unique topics include finite-time behavior multiple timescales and asynchronous implementation. There is a useful plethora of applications each with concrete examples from engineering and economics. Notably it covers variants of stochastic gradient-based optimization schemes fixed-point solvers which are commonplace in learning algorithms for approximate dynamic programming and some models of collective behavior.